Human-machine Communication Research Articles

Collaboration of Artificial Intelligence and Journalists in Online Media from the Perspective of Human-Machine Communication

This study aims to analyze the collaboration between Artificial Intelligence (AI) and journalists in online media using the Human-Machine Communication (HMC) approach. The research method used is a literature study. Data is processed using Miles and Huberman data analysis techniques. The results of this study indicate that AI is used in three primary stages of journalism: news gathering, news production, and news distribution. At the news gathering stage, AI assists journalists in collecting news materials from various sources and analyzing audience interest in specific topics. AI is used in news script creation, editing, and proofreading at the news production stage. AI chatbots and NLP programs help with automatic news writing and factual verification. Meanwhile, at the news distribution stage, AI is used for content personalization, news recommendations, and SEO optimization in online media. From the HMC perspective, collaboration between AI and journalists can be conceptualized as a unidirectional and two-way process. Collaboration between AI and journalists in a social context also occurs at the micro, meso, and macro levels, where interactions between humans and machines affect the social situation, the immediate reality of individuals, and the structure of society as a whole.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

A self-damping triboelectric tactile patch for self-powered wearable electronics

Wearable tactile sensing systems with bionic designs holds significant promise for environmental interactions and human–machine communication. Triboelectric sensing technology plays a vital role in acquiring and quantifying tactile signals. Conventional elastic sensing materials, however, lack damping performance and are easily damaged by vibrations, leading to sensor failure. To address this challenge, our study proposes a highly damping triboelectric gel based on a hydrogen bonding assisted microphase separation strategy. In microphase separation, the soft phase provides the viscoelasticity needed for the gel, while the hard phase dissipates shock energy. This energy dissipation mechanism enables the gel to achieve excellent damping performance (tan δ = 0.68 at 1Hz), skin-like softness (Young’s modulus of 130 kPa), and stretchability (> 900%). The resulting self-damping tactile patch effectively absorbs and dissipates external vibrations, ensuring a stable and reliable wearable tactile sensing device. This work provides new insights into the application of triboelectric gels in wearable electronics.

НЕЙРОМЕРЕЖЕВІ МАТЕМАТИЧНІ МОДЕЛІ ЛЮДИНОМАШИННОГО СПІЛКУВАННЯ ТА РОЗПІЗНАВАННЯ

The study is devoted to the neural network interpretation of the task of human-machine communication and recognition by multiple criteria, considered as a task of assignment. The main goal is to reduce this problem to a standard form, where the number of criteria groups is equal to the number of ranked documents. The study defined the architecture of a neural network and proposed the use of a network of binary neurons, which is a matrix of a certain dimension. The proposed ranking model is based on a neural network that contains arbitrary feedback. This allows the excitation to be transmitted back to the neuron, which contributes to the repeated performance of its function. However, in dynamic neural networks instability occurs, which is manifested in a random change in the states of neurons without reaching stationary states. The question of stability of the dynamics of such systems remains open. The considered discrete Hopfield neural network has the following characteristics: one layer of elements, each element is connected to all others, but not to itself; only one element is updated per stage; elements are updated in random order, but each is updated with the same frequency; the output function is binary (value "0" or "1"). A Hopfield neural network is recurrent: the output of the network is reused as input until a steady state is reached. After starting, the neural network changes its state, gradually moving to a stable mode, which allows identifying a plan for evaluating the process of human-machine communication according to a set of criteria. Random search procedures are used to refine the results. The proposed energy function is minimized to ensure that the constraints are met and the problem is solved. The constructed function reaches a minimum only in the states corresponding to the assignment plans. The definition of the network parameters is carried out by comparing the obtained functions with the energy function in general form. The practical implementation of the model demonstrated that Hopfield's neural network can be successfully applied to document ranking in human-machine communication and recognition systems, providing high accuracy and efficiency in solving ranking problems.

Algorithms as conversational partners: Looking at Google auto-predict through the lens of symbolic interaction

This article showcases a speculative methodology for recreating interactions between a human and Google Search’s Auto-Predict interface as conversations, to explore how AI-based systems are both persuasive and deeply personal. Using ethnomethodology tools and a symbolic interactionist lens, the paper presents three versions of a single Google search, each variation building a slightly different angle on the plausible utterances and interpersonal dynamics of the human and nonhuman partners. This thought experiment emerges from a decade of classroom-based digital literacy exercises with young adults, training them to analyze their lived experiences with digital media, algorithms, and devices. Transforming information exchanges into personal conversations provides a creative method for analyzing how relations are co-constructed in the granular processes of interaction, through which mutual intelligibility is built, meaning about the world is made, and identities are formed. This critical analysis extends methods for human–machine communication studies and elaborates notions of algorithmic identity.

Pedagogical agents communicating and scaffolding students' learning: High school teachers' and students' perspectives

Pedagogical agents (PAs) communicate verbally and non-verbally with students in digital and virtual reality/augmented reality learning environments. PAs have been shown to be beneficial for learning, and generative artificial intelligence, such as large language models, can improve PAs' communication abilities significantly. K-12 education is underrepresented in learning technology research and teachers' and students' insights have not been considered when developing PA communication. The current study addresses this research gap by conducting and analyzing semi-structured, in-depth interviews with eleven high school teachers and sixteen high school students about their expectations for PAs' communication capabilities. The interviewees identified relational and task-related communication capabilities that a PA should perform to communicate effectively with students and scaffold their learning. PA communication that is simultaneously affirmative and relational can induce immediacy, foster the relationship and engagement with a PA, and support students' learning management. Additionally, the teachers and students described the activities and technological aspects that should be considered when designing conversational PAs. The study showed that teachers and students applied human-to-human communication scripts when outlining their desired PA communication characteristics. The study offers novel insights and recommendations to researchers and developers on the communicational, pedagogical, and technological aspects that must be considered when designing communicative PAs that scaffold students’ learning, and discusses the contributions on human–machine communication in education.

La filosofia alla prova dei linguaggi artificiali

Introdution: In recent years, the philosophy of language has undergone significant transformations thanks to developments in artificial intelligence and brain-machine interfaces (BMI). The introduction of the fourth version of Chat GPT and advancements in BMIs, such as Elon Musk's Neuralink, mark the beginning of a new era in human-machine communication. This study compares natural and artificial languages in light of recent debates on the use of artificial intelligence for the simulation of natural languages (Large Language Models, LLM). Metodologhia: The analysis reveals that Chat GPT technology is complementary to, rather than competitive with, human linguistic capabilities. LLMs excel in formal linguistic competence but are limited in the functional understanding of language, highlighting the distinction between language processing in the human brain and the functioning of LLMs. Furthermore, BMIs are opening new possibilities for direct communication between human brains and machines, as demonstrated by BrainNet projects. Results: These developments raise ethical and philosophical questions regarding the ownership of thoughts and individual integrity. Methodologically, the study examines neuroscientific research through philosophical analysis to highlight theoretical-conceptual implications. Conclusion: The conclusions suggest that while advanced technologies offer new communication perspectives, a thorough reflection on ethical and legal issues is necessary for responsible use.

AI in the newsroom: a collective case study about newsworker-AI collaboration in the German newspaper industry

ABSTRACT The adoption of AI in newsrooms can fundamentally change journalistic processes. Nowadays, AI is increasingly able to fulfil journalistic tasks, evolving the roles and interaction of newsworkers and AI. Given its high impact, the question arises how AI and newsworkers can collaborate productively in the newsroom to maintain or even improve journalistic quality. This study analyses newsworker-AI collaboration at four German newspaper publishers through the lens of Human-Machine Communication (HMC). Based on the data, a model for the evaluation of newsworker-AI collaboration is developed and validated. The results show that the interaction intensity between newsworkers and AI as well as the creation of knowledge in the newsroom can be identified as relevant evaluation criteria for newsworker-AI collaboration.

Can you repeat that again? Investigating the mediating effects of perceived accommodation appropriateness for accommodative voice-based assistants

The widespread use of Voice-Based Assistants (VBAs) in various applications has introduced a new dimension to human-machine communication. This study explores how users assess VBAs exhibiting either excessive or insufficient communication accommodation in imagined initial interactions. Drawing on Communication Accommodation Theory (CAT) and the Stereotype Content Model (SCM), the present research investigates the mediation effect of perceived accommodation on the relationship between warmth and competence of the SCM and evaluations of the VBA as a communicator and a speaker. Participants evaluated the underaccommodative VBA significantly lower with respect to its communication and evaluations of the VBA as a speaker, which were indirectly predicted by warmth and competence stereotype content models via the perceived appropriateness of the communication. The implications of our findings and future research are discussed.

Visioning a two-level human–machine communication framework: initiating conversations between explainable AI and communication

Abstract Amid mounting interest in artificial intelligence (AI) technology, communication scholars have sought to understand humans’ perceptions of and attitudes toward AI’s predictions, recommendations, and decisions. Meanwhile, scholars in the nascent but growing field of explainable AI (XAI) have aimed to clarify AI’s operational mechanisms and make them interpretable, visible, and transparent. In this conceptual article, we suggest that a conversation between human–machine communication (HMC) and XAI is advantageous and necessary. Following the introduction of these two areas, we demonstrate how research on XAI can inform the HMC scholarship regarding the human-in-the-loop approach and the message production explainability. Next, we expound upon how communication scholars’ focuses on message sources, receivers, features, and effects can reciprocally benefit XAI research. At its core, this article proposes a two-level HMC framework and posits that bridging the two fields can guide future AI research and development.

Gender effect in human-machine communication: a neurophysiological study.

This study aimed to investigate the neural mechanism by which virtual chatbots' gender might influence users' usage intention and gender differences in human-machine communication. Event-related potentials (ERPs) and subjective questionnaire methods were used to explore the usage intention of virtual chatbots, and statistical analysis was conducted through repeated measures ANOVA. The findings of ERPs revealed that female virtual chatbots, compared to male virtual chatbots, evoked a larger amplitude of P100 and P200, implying a greater allocation of attentional resources toward female virtual chatbots. Considering participants' gender, the gender factors of virtual chatbots continued to influence N100, P100, and P200. Specifically, among female participants, female virtual chatbots induced a larger P100 and P200 amplitude than male virtual chatbots, indicating that female participants exhibited more attentional resources and positive emotions toward same-gender chatbots. Conversely, among male participants, male virtual chatbots induced a larger N100 amplitude than female virtual chatbots, indicating that male participants allocated more attentional resources toward male virtual chatbots. The results of the subjective questionnaire showed that regardless of participants' gender, users have a larger usage intention toward female virtual chatbots than male virtual chatbots. Our findings could provide designers with neurophysiological insights into designing better virtual chatbots that cater to users' psychological needs.

Human–computer pragmatics trialled: some (im)polite interactions with ChatGPT 4.0 and the ensuing implications

ABSTRACT Upon rapid evolution, ChatGPT can now generate content that is linguistically accurate and logically sound, while sidestepping ethical, social and legal concerns. This research seeks to investigate whether ChatGPT will employ different pragmatic strategies in its responses to (im)polite questions. In our experiment, this AI-powered tool was instructed to answer 200 self-made questions over four (im)politeness levels, and the 200 responses were collected to go through linguistic and sentiment analysis. Triangulated data, together with typical examples, show that ChatGPT tends to give shorter and less positive answers to less polite questions, appearing to be less responsive when confronted with more blunt and offensive inquiries. This, to some extent, resembles how human beings react when treated impolitely. A tentative explanation may be that, given its nature as a large language model, ChatGPT mirrors human interaction in various scenarios, and draws on prevalent human communication tendencies. Thus, interacting with ChatGPT is more of a human-society interaction than human-machine communication in the real sense. Our research sheds light on the coined “human-machine pragmatics”, i.e. how humans can best communicate with computers for the best informative and affective outcomes. The implications for language education are also discussed in the end.

Speech Cloning: Text-To-Speech Using VITS

Voice is one of the most common and natural communication methods for humans. Voice is becoming the primary interface for AI voice assistants like Amazon Alexa, as well as in autos and smart home devices. Homes and so on. As human-machine communication becomes more common, researchers are exploring technology that mimics genuine speech. Speech cloning is the practice of copying or mimicking another person's speech, usually utilizing modern technology and artificial intelligence (AI). This entails producing a synthetic or cloned version of someone's voice that sounds very similar to the actual speaker. The objective is to produce speech that is indistinguishable from the genuine person, both in tone and intonation. Instant Voice Cloning (IVC) in text-to-speech (TTS) synthesis refers to the TTS model's capacity to copy the voice of any reference speaker based on a short audio sample, without requiring extra speaker-specific training. This method is usually referred to as zero-shot TTS. IVC provides users with the flexibility to tailor the generated voice, offering significant value across diverse real-world applications. Examples include media content creation, personalized chatbots, and multi-modal interactions between humans and computers or extensive language models.

Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

Human–robot interaction is becoming increasingly common to perform useful tasks in everyday life. From the human–machine communication perspective, achieving effective interaction in natural language is one challenge. To address it, natural language processing strategies have recently been used, commonly following a supervised machine learning framework. In this context, most approaches rely on the use of linguistic resources (e.g., taggers or embeddings), including training corpora. Unfortunately, such resources are scarce for some languages in specific domains, increasing the complexity of solution approaches. Motivated by these challenges, this paper explores deep learning methods for understanding natural language commands emitted to service robots that guide their movements in low-resource scenarios, defined by the use of Spanish and Nahuatl languages, for which linguistic resources are scarcely unavailable for this specific task. Particularly, we applied natural language understanding (NLU) techniques using deep neural networks and transformers-based models. As part of the research methodology, we introduced a labeled dataset of movement commands in the mentioned languages. The results show that models based on transformers work well to recognize commands (intent classification task) and their parameters (e.g., quantities and movement units) in Spanish, achieving a performance of 98.70% (accuracy) and 96.96% (F1) for the intent classification and slot-filling tasks, respectively). In Nahuatl, the best performance obtained was 93.5% (accuracy) and 88.57% (F1) in these tasks, respectively. In general, this study shows that robot movements can be guided in natural language through machine learning models using neural models and cross-lingual transfer strategies, even in low-resource scenarios.

A Comprehensive Analysis of AI/ML-enabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADAS) are changing driver-vehicle interactions to improve road safety and reduce distractions. Technological advances like ADAS and AI in cars present societal challenges and opportunities. It shows how AI aids human-machine communication by improving motor skills. The auto industry is interested in ADAS because it can increase energy efficiency, safety, and comfort. Numerous studies have shown its benefits. ADAS and vehicle networking show promise, but establishing a sound control system is challenging. Model Predictive Control (MPC) is one answer to these problems. To manage higher-level connectivity and automation, the paper analyses and implements key research. It also finds issues and recommends solutions. The latest driverless car ADAS improvements have dramatically increased passenger safety. These systems are safer and more automated using sensors and ECUs. Most ADAS have RADAR, cameras, ultrasonic, and LiDAR. This work uses AI/ML-enabled Predictive Maintenance modelling to improve ADAS safety and longevity. AI and ML in Advanced Driver Assistance Systems (ADAS) are significant vehicle safety and reliability advances. AI/ML-enabled predictive maintenance detects and fixes ADAS component faults. ADAS predictive maintenance using AI/ML can detect issues, improve driver safety, and boost vehicle efficiency. Advanced sensor arrays and control units are needed for adaptive cruise control, traffic sign recognition, and lane-keeping assistance. AI/ML algorithms discover issues and enable early interventions in predictive maintenance models. Predictive maintenance is examined utilizing classical machine learning, deep learning, and reinforcement learning. Integration of numerous AI/ML models, real-time data processing, customization based on vehicle usage patterns, scalability, and adaptability of predictive maintenance models to new ADAS technologies are research gaps.

Interaction Process Between Humans and ChatGPT in the Context of Interpersonal Communication

This study examines human interaction with artificial intelligence technology, focusing on the implementation of ChatGPT, a chatbot developed by OpenAI. Through the Human-Machine Communication (HMC) approach, the research describes human-like attributes in ChatGPT, exploring emotional responses and utility in educational, professional, and personal contexts. Qualitative research methods with triangulation techniques were used for a holistic understanding, involving interviews, observations, and document analysis. The results indicate that ChatGPT can provide adaptive responses, adjusting language style, and presenting information with self-awareness. Comparisons between human-to-human and human-to-machine interactions, particularly through ChatGPT, reveal significant differences. In human-to-human communication, the primary role is given to humans as message sources, while in machine communication, ChatGPT becomes an interactive partner, especially in text messages. Despite similarities in interpersonal communication features, such as feedback, personal relationship cues are more pronounced in human-to-human interactions. Factors like self-concept, openness, and confidence are dominant in human-to-human communication, while AI literacy becomes crucial in interactions with machines. Keywords: Human-Machine Communication, ChatGPT, Interaction, Interpersonal

Credibility and altered communication styles of AI graders in the classroom

AbstractBackgroundEducation is often the primary arena for exploring and integrating new technologies. AI and human‐machine communication (HMC) are prevalent in the classroom, yet we are still learning how student perceptions of these tools will impact education.ObjectivesWe sought to understand student perceptions of credibility related to written feedback attributed to a human or an AI grader (Study One). We also investigated how corrective messages containing verbal immediacy and social support influenced student perceptions of an AI grader's credibility based on feedback in an evaluated essay (Study Two).MethodsWe used an online experimental design to assess the perceived credibility of a grader. In Study One, we randomly assigned students (N = 155) to a condition that contained a paragraph they were told was evaluated by a human or an AI grader. In Study Two (N = 222), we investigated ways of increasing perceptions of an AI grader's credibility by writing messages with higher/lower levels of immediacy and social support.ResultsIn Study One, the students rated both the human and AI grader as credible (yet rated the AI grader lower on goodwill). The data suggest that students in Study Two attributed more goodwill (i.e., caring) to the AI grader when the feedback included more verbal immediacy.ConclusionsOur results highlight the importance of student perceptions and communication styles when integrating technology into education. The two studies imply that students viewed the human and AI graders as competent, caring, and trustworthy, specifically when feedback included more immediacy cues.

A Survey on Speech Emotion Recognition System using CNN Algorithm

The speech emotion recognition system (SER) plays an important role in decoding and predicting the speaker's emotional state by analyzing audio signals. Emotions are often simplified and grouped into categories such as anger, happiness, sadness, and even neutral emotional states. These emotions serve to communicate one's thoughts and provide insight into one's physical and mental health. Speech is the most basic and natural form of communication when interacting with others. Voice signals enable rapid communication between individuals, making them a valuable and effective method of expression. Over the past decades, countless research initiatives have been devoted to the development of voice-based automatic emotion recognition systems, especially to improve human-machine communication. Voice is gradually taking center stage in the field of Human-Machine interfaces in the IT sector. This interdisciplinary field draws on a variety of fields, including computer science, signal processing, psychology, linguistics, and more. As technology advances, it allows for seamless communication between humans and machines. Speech recognition not only interprets speech but also captures nuances in a person's tone and expressions, similar to body language. Therefore, it becomes an essential element of the Human-Machine communication system.

5G NB-IoT System Integrated with High-Performance Fiber Sensor Inspired by Cirrus and Spider Structures.

Real-time telemedicine detection can solve the problem of the shortage of public medical resources caused by the coming aging society. However, the development of such an integrated monitoring system is hampered by the need for high-performance sensors and the strict-requirement of long-distance signal transmission and reproduction. Here, a bionic crack-spring fiber sensor (CSFS) inspired by spider leg and cirrus whiskers for stretchable and weavable electronics is reported. Trans-scale conductive percolation networks of multilayer graphene around the surface of outer spring-like Polyethylene terephthalate (PET) fibers and printing Ag enable a high sensitivity of 28475.6 and broad sensing range over 250%. The electromechanical changes in different stretching stages are simulated by Comsol to explain the response mechanism. The CSFS is incorporated into the fabric and realized the human-machine interactions (HMIs) for robot control. Furthermore, the 5G Narrowband Internet of Things (NB-IoT) system is developed for human healthcare data collection, transmission, and reproduction together with the integration of the CSFS, illustrating the huge potential of the approach in human-machine communication interfaces and intelligent telemedicine rehabilitation and diagnosis monitoring.

Visual Questioner using Bert Transformer

The Visual Questioner, an innovative machine learning endeavor, represents a cutting-edge project designed to process images and facilitate user interaction through AI-generated responses to inquiries. Focused on the fusion of visual recognition and natural language understanding, the overarching objective of this project is to cultivate a robust model capable of discerning intricate details within an image and, subsequently, formulating coherent textual responses to user-generated questions. At the core of the Visual Questioner lies the imperative to bridge the semantic gap between visual content and linguistic expression, enhancing the depth of comprehension and interaction within the artificial intelligence paradigm. By harnessing advanced algorithms and neural network architectures, this initiative aspires to elevate the sophistication of image-based question-answering systems, presenting a pivotal advancement in the realm of human-machine communication. This research not only delves into the technical intricacies of image understanding but also underscores the imperative role of natural language generation, thereby contributing substantively to the evolving landscape of AI applications. As we navigate through the complexities of this project, its potential implications span diverse domains, ranging from human-computer interaction to autonomous systems and beyond, underscoring the multifaceted significance of the Visual Questioner in contemporary artificial intelligence research.